On Thursday, August 24, LNS Research hosted the webcast, “Quality 4.0: What You Should Know, How to Get There.” The presentation examined what quality innovation leaders are doing with data, analytics, connectivity, and apps to achieve continuous improvement and considerable operational and financial performance advantages. The discussion included an examination of how to build an effective strategy to reach Quality 4.0.
Q1: We’ve been deploying a new analytics strategy, where we’re generating real time quality metrics and trend analysis. How is that different than what you’re talking about here?
A1: Congratulations – that’s a big step forward. Many manufacturers do not have real-time visibility to metrics, which is a top challenge preventing quality from attaining its objectives. When generating real-time metrics, quality teams define the relationships and metrics they are looking for and can generate metrics such as a number of open corrective actions, First Pass Yield, number of rejected lots by root-cause in a current month, defects by the supplier, etc.
This a valuable traditional quality initiative. However, it is not a Quality 4.0 initiative. In Quality 4.0, the manufacturer would deploy Machine Learning / Artificial Intelligence (ML/AI) tools that can automatically detect patterns in a data set that could include time-series data as well as business systems. For instance, manufacturers identify acceptance ranges for product testing. While these acceptance ranges prevent the majority of defects, there are products that pass such testing but still fail within the warranty period. A manufacturer might apply ML/AI to product test, manufacturing, supplier and warranty data to identify test patterns that correlate to warranty failure. These patterns are identified by the algorithms and will predict potentially bad components, possibly traced to manufacturing data or supplier data.
Q2: My team is working on an integrated data model for Supplier Quality, a supplier portal, analytics, collaboration, etc. I see these things in your framework, but am not sure how Quality 4.0 changes these.
A2: You should see those elements in the framework because Quality 4.0 doesn’t replace traditional quality. It builds on it. All of the items you are discussing are valuable traditional quality initiatives.
LNS recommends that manufacturers get educated on Quality 4.0, identify how to re-align existing strategies to corporate digital transformation initiatives, and the lead the development and execution of Digital Transformation strategies around quality improvement and monitoring.
To update the strategies from traditional quality to Quality 4.0, LNS recommends leveraging the Digital Transformation framework, and the Quality 4.0 Operational Excellence framework, to ensure that traditional quality initiatives are updated to reflect Quality 4.0
Q3: My company has some initiatives around increasing predictability and getting more from data, but it's early on. How should I get involved?
A3: Perfect – these are Digital Transformation initiatives. The same thing is true if your company has a “connected home,” or “connected products,” or “smart manufacturing” initiative. Many manufacturers are beginning on this journey, but often the roadmaps aren’t set yet. This is absolutely a perfect time to get educated, get involved, and lead.
There’s a very real danger to not getting involved. Do you recall how much of the market started deploying ERP in the mid-to-late ‘90’s? Quality wasn’t usually involved in these decisions and didn’t provide their use cases, and therefore were deprioritized for over a decade. This is one reason that quality technology is usually behind the curve of other functions.
It’s very important for quality teams to get their voices heard and not leave this to other departments. Timing is very important. Most are still in the early stages, which is a great time to act.
Q4: How does an organization transition experiential information in ops folks head into a digital environment? / How do you deal with institutional knowledge?
A4: This is an important element of Quality 4.0. Many quality leaders have quality competency initiatives underway to make quality expertise, particularly special knowledge/skills, available more globally. A valuable “zero tech” approach to this is has been centers of excellence, where subject matter experts can share knowledge with each other and with knowledge seekers across the organization. One challenge for centers of excellence is the tradeoff between cost, capacity, and availability. Subject matter experts (SMEs) are expensive; they can only work so many projects at once, and therefore cannot be available for all projects all the time.
A traditional quality tech solution is to automate processes, centralize data repositories, and build reusable libraries (e.g., risk registers) overseen by SMEs. With systems such as these, global knowledge seekers can query “a” system (ideally should be one system or at least transparently appear to be one system) that contains the subject matter expertise. Also, with reusable libraries such as risk registers, that knowledge can be built into the processes so that no seeking is necessary.
Quality 4.0 opens a number of doors related to this. First, near-real-time transparent data, interpreted through analytics, and made accessible through apps can provide greater visibility to actual performance to a broad audience, helping democratize knowledge and insights. Secondly, new levels of collaboration such as social media provide open many-to-many knowledge sharing. Finally, technologies such as blockchain also provide deeper insights.
Q5: Which software typically EQMS will power Quality 4.0?
A5: There are a number of technologies that can come to play in Quality 4.0. As suggested, EQMS – Enterprise Quality Management Software – is one important technology. As mentioned in the webcast, this is important to get high veracity data, particularly in quality where there may be many dozens of systems that do not “talk.” Analytics are also critical, in order to drive the new insights from the high veracity data. Connectivity, cloud, apps, etc, are all critical.
LNS groups these into buckets around data, analytics, scalability, app development, connectivity, and collaboration. I’d recommend you download the eBook to gain a deeper appreciation of these technologies.